Bioelectrical Signal Processing in Cardiology: inverse solution mapping on epicardial and endocardial potentials
Bibliographic record
Abstract
Inverse electrocardiography is aimed at reconstructing the heart electrical activity and its corresponding spread throughout the heart from non-invasive body surface measurements obtained through the Electrocardiogram (ECG) technique. This field has showed increasing promise during the last decades due to its main application: predict possible cardiac diseases such that medical interventions are avoided and costs which were meant to these clinical operations are also reduced. Still, properly detection of these cardiac diseases is directly correlated with the performance of the inverse-solving regularization methods. This Final Project counts with the collaboration of the Department of Medicine and the Department of Mathematics and Statistics of the Dalhousie University, Canada, which provided us with subject-specific BSPM measurements and data extracted from CT-scans of catheter interventions. Hence, our principals goals would be the analysis, review, comparison and cross-validation of the computed results on the multiple variants of the automated inverse-solving algorithms. Accordingly, a final conclusion of the influence which every regularization method has on the inverse solutions would be conducted by means of specialized simulation programs and numerical error measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".